def main():
print("init data folders")
print(METHOD)
encoder_lv1 = models.Encoder().apply(weight_init).cuda(GPU)
encoder_lv2 = models.Encoder().apply(weight_init).cuda(GPU)
encoder_lv3 = models.Encoder().apply(weight_init).cuda(GPU)
decoder_lv1 = models.Decoder().apply(weight_init).cuda(GPU)
decoder_lv2 = models.Decoder().apply(weight_init).cuda(GPU)
decoder_lv3 = models.Decoder().apply(weight_init).cuda(GPU)
if os.path.exists(str('./checkpoints/' + METHOD + "/encoder_lv1.pkl")):
encoder_lv1.load_state_dict(
torch.load(str('./checkpoints/' + METHOD + "/encoder_lv1.pkl")))
print("load encoder_lv1 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/encoder_lv2.pkl")):
encoder_lv2.load_state_dict(
torch.load(str('./checkpoints/' + METHOD + "/encoder_lv2.pkl")))
print("load encoder_lv2 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/encoder_lv3.pkl")):
encoder_lv3.load_state_dict(
torch.load(str('./checkpoints/' + METHOD + "/encoder_lv3.pkl")))
print("load encoder_lv3 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/decoder_lv1.pkl")):
decoder_lv1.load_state_dict(
torch.load(str('./checkpoints/' + METHOD + "/decoder_lv1.pkl")))
print("load encoder_lv1 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/decoder_lv2.pkl")):
decoder_lv2.load_state_dict(
torch.load(str('./checkpoints/' + METHOD + "/decoder_lv2.pkl")))
print("load decoder_lv2 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/decoder_lv3.pkl")):
decoder_lv3.load_state_dict(
torch.load(str('./checkpoints/' + METHOD + "/decoder_lv3.pkl")))
print("load decoder_lv3 success")
if os.path.exists('./test_results/' + EXPDIR) == False:
os.system('mkdir ./test_results/' + EXPDIR)
iteration = 0.0
test_time = 0.0
total_psnr = 0
total_ssim = 0
print("Testing............")
print("===========================")
test_dataset = GoProDataset(
blur_image_files='./datas/GoPro/test_blur_file.txt',
sharp_image_files='./datas/GoPro/test_sharp_file.txt',
root_dir='./datas/GoPro',
transform=transforms.Compose([transforms.ToTensor()]))
test_dataloader = DataLoader(test_dataset, batch_size=1, shuffle=False)
total_psnr = 0
for iteration, images in enumerate(test_dataloader):
with torch.no_grad():
images_lv1 = Variable(images['blur_image'] - 0.5).cuda(GPU)
start = time.time()
H = images_lv1.size(2)
W = images_lv1.size(3)
images_lv2_1 = images_lv1[:, :, 0:int(H / 2), :]
images_lv2_2 = images_lv1[:, :, int(H / 2):H, :]
images_lv3_1 = images_lv2_1[:, :, :, 0:int(W / 2)]
images_lv3_2 = images_lv2_1[:, :, :, int(W / 2):W]
images_lv3_3 = images_lv2_2[:, :, :, 0:int(W / 2)]
images_lv3_4 = images_lv2_2[:, :, :, int(W / 2):W]
feature_lv3_1 = encoder_lv3(images_lv3_1)
feature_lv3_2 = encoder_lv3(images_lv3_2)
feature_lv3_3 = encoder_lv3(images_lv3_3)
feature_lv3_4 = encoder_lv3(images_lv3_4)
feature_lv3_top = torch.cat((feature_lv3_1, feature_lv3_2), 3)
feature_lv3_bot = torch.cat((feature_lv3_3, feature_lv3_4), 3)
feature_lv3 = torch.cat((feature_lv3_top, feature_lv3_bot), 2)
residual_lv3_top = decoder_lv3(feature_lv3_top)
residual_lv3_bot = decoder_lv3(feature_lv3_bot)
feature_lv2_1 = encoder_lv2(images_lv2_1 + residual_lv3_top)
feature_lv2_2 = encoder_lv2(images_lv2_2 + residual_lv3_bot)
feature_lv2 = torch.cat(
(feature_lv2_1, feature_lv2_2), 2) + feature_lv3
residual_lv2 = decoder_lv2(feature_lv2)
feature_lv1 = encoder_lv1(images_lv1 + residual_lv2) + feature_lv2
deblur_image = decoder_lv1(feature_lv1)
psnr = compare_psnr(images['sharp_image'].numpy()[0],
deblur_image.detach().cpu().numpy()[0] + 0.5)
# ssim = SSIM(np.transpose(images['sharp_image'].numpy()[0], (1, 2, 0)), np.transpose(deblur_image.detach().cpu().numpy()[0], (1, 2, 0))+0.5, multichannel=True, data_range=1.0)
pilReal = Image.fromarray(
((np.transpose(images['sharp_image'].numpy()[0],
(1, 2, 0))) * 255).astype(np.uint8))
pilFake = Image.fromarray(
((np.transpose(deblur_image.detach().cpu().numpy()[0],
(1, 2, 0)) + 0.5) * 255).astype(np.uint8))
ssim = SSIM(pilFake).cw_ssim_value(pilReal)
total_psnr += psnr
total_ssim += ssim
stop = time.time()
test_time += stop - start
print("testing...... iteration: ", iteration, ", psnr: ", psnr,
", ssim: ", ssim)
iteration += 1
print("PSNR is: ", (total_psnr / 1111))
print("SSIM is: ", (total_ssim / 1111))
def main():
print("init data folders")
torch.backends.cudnn.benchmark = True
torch.cuda.empty_cache()
encoder_lv1 = models.Encoder()
encoder_lv2 = models.Encoder()
encoder_lv3 = models.Encoder()
encoder_lv4 = models.Encoder()
decoder_lv1 = models.Decoder()
decoder_lv2 = models.Decoder()
decoder_lv3 = models.Decoder()
decoder_lv4 = models.Decoder()
encoder_lv1.apply(weight_init).cuda(GPU)
encoder_lv2.apply(weight_init).cuda(GPU)
encoder_lv3.apply(weight_init).cuda(GPU)
encoder_lv4.apply(weight_init).cuda(GPU)
decoder_lv1.apply(weight_init).cuda(GPU)
decoder_lv2.apply(weight_init).cuda(GPU)
decoder_lv3.apply(weight_init).cuda(GPU)
decoder_lv4.apply(weight_init).cuda(GPU)
encoder_lv1_optim = RAdam(encoder_lv1.parameters(), lr=LEARNING_RATE)
encoder_lv1_scheduler = StepLR(encoder_lv1_optim,
step_size=1000,
gamma=0.1)
encoder_lv2_optim = RAdam(encoder_lv2.parameters(), lr=LEARNING_RATE)
encoder_lv2_scheduler = StepLR(encoder_lv2_optim,
step_size=1000,
gamma=0.1)
encoder_lv3_optim = RAdam(encoder_lv3.parameters(), lr=LEARNING_RATE)
encoder_lv3_scheduler = StepLR(encoder_lv3_optim,
step_size=1000,
gamma=0.1)
encoder_lv4_optim = RAdam(encoder_lv4.parameters(), lr=LEARNING_RATE)
encoder_lv4_scheduler = StepLR(encoder_lv4_optim,
step_size=1000,
gamma=0.1)
decoder_lv1_optim = RAdam(decoder_lv1.parameters(), lr=LEARNING_RATE)
decoder_lv1_scheduler = StepLR(decoder_lv1_optim,
step_size=1000,
gamma=0.1)
decoder_lv2_optim = RAdam(decoder_lv2.parameters(), lr=LEARNING_RATE)
decoder_lv2_scheduler = StepLR(decoder_lv2_optim,
step_size=1000,
gamma=0.1)
decoder_lv3_optim = RAdam(decoder_lv3.parameters(), lr=LEARNING_RATE)
decoder_lv3_scheduler = StepLR(decoder_lv3_optim,
step_size=1000,
gamma=0.1)
decoder_lv4_optim = RAdam(decoder_lv4.parameters(), lr=LEARNING_RATE)
decoder_lv4_scheduler = StepLR(decoder_lv4_optim,
step_size=1000,
gamma=0.1)
if os.path.exists(str('./checkpoints/' + METHOD + "/encoder_lv1.pkl")):
encoder_lv1.load_state_dict(
torch.load(str('./checkpoints/' + METHOD + "/encoder_lv1.pkl")))
print("load encoder_lv1 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/encoder_lv2.pkl")):
encoder_lv2.load_state_dict(
torch.load(str('./checkpoints/' + METHOD + "/encoder_lv2.pkl")))
print("load encoder_lv2 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/encoder_lv3.pkl")):
encoder_lv3.load_state_dict(
torch.load(str('./checkpoints/' + METHOD + "/encoder_lv3.pkl")))
print("load encoder_lv3 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/encoder_lv4.pkl")):
encoder_lv4.load_state_dict(
torch.load(str('./checkpoints/' + METHOD + "/encoder_lv4.pkl")))
print("load encoder_lv4 success")
# for param in decoder_lv4.layer24.parameters():
# param.requires_grad = False
# for param in encoder_lv3.parameters():
# param.requires_grad = False
# # print("检查部分参数是否固定......")
# print(encoder_lv3.layer1.bias.requires_grad)
# for param in decoder_lv3.parameters():
# param.requires_grad = False
# for param in encoder_lv2.parameters():
# param.requires_grad = False
# # print("检查部分参数是否固定......")
# print(encoder_lv2.layer1.bias.requires_grad)
# for param in decoder_lv2.parameters():
# param.requires_grad = False
if os.path.exists(str('./checkpoints/' + METHOD + "/decoder_lv1.pkl")):
decoder_lv1.load_state_dict(
torch.load(str('./checkpoints/' + METHOD + "/decoder_lv1.pkl")))
print("load encoder_lv1 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/decoder_lv2.pkl")):
decoder_lv2.load_state_dict(
torch.load(str('./checkpoints/' + METHOD + "/decoder_lv2.pkl")))
print("load decoder_lv2 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/decoder_lv3.pkl")):
decoder_lv3.load_state_dict(
torch.load(str('./checkpoints/' + METHOD + "/decoder_lv3.pkl")))
print("load decoder_lv3 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/decoder_lv4.pkl")):
decoder_lv4.load_state_dict(
torch.load(str('./checkpoints/' + METHOD + "/decoder_lv4.pkl")))
print("load decoder_lv4 success")
if os.path.exists('./checkpoints/' + METHOD) == False:
os.system('mkdir ./checkpoints/' + METHOD)
for epoch in range(args.start_epoch, EPOCHS):
epoch += 1
print("Training...")
print('lr:', encoder_lv1_scheduler.get_lr())
train_dataset = GoProDataset(
blur_image_files='./datas/GoPro/train_blur_file.txt',
sharp_image_files='./datas/GoPro/train_sharp_file.txt',
root_dir='./datas/GoPro',
crop=True,
crop_size=IMAGE_SIZE,
transform=transforms.Compose([transforms.ToTensor()]))
train_dataloader = DataLoader(train_dataset,
batch_size=BATCH_SIZE,
shuffle=True,
num_workers=8,
pin_memory=True)
start = 0
for iteration, images in enumerate(train_dataloader):
mse = nn.MSELoss().cuda(GPU)
gt = Variable(images['sharp_image'] - 0.5).cuda(GPU)
H = gt.size(2)
W = gt.size(3)
images_lv1 = Variable(images['blur_image'] - 0.5).cuda(GPU)
images_lv2_1 = images_lv1[:, :, 0:int(H / 2), :]
images_lv2_2 = images_lv1[:, :, int(H / 2):H, :]
images_lv3_1 = images_lv2_1[:, :, :, 0:int(W / 2)]
images_lv3_2 = images_lv2_1[:, :, :, int(W / 2):W]
images_lv3_3 = images_lv2_2[:, :, :, 0:int(W / 2)]
images_lv3_4 = images_lv2_2[:, :, :, int(W / 2):W]
images_lv4_1 = images_lv3_1[:, :, 0:int(H / 4), :]
images_lv4_2 = images_lv3_1[:, :, int(H / 4):int(H / 2), :]
images_lv4_3 = images_lv3_2[:, :, 0:int(H / 4), :]
images_lv4_4 = images_lv3_2[:, :, int(H / 4):int(H / 2), :]
images_lv4_5 = images_lv3_3[:, :, 0:int(H / 4), :]
images_lv4_6 = images_lv3_3[:, :, int(H / 4):int(H / 2), :]
images_lv4_7 = images_lv3_4[:, :, 0:int(H / 4), :]
images_lv4_8 = images_lv3_4[:, :, int(H / 4):int(H / 2), :]
feature_lv4_1 = encoder_lv4(images_lv4_1)
feature_lv4_2 = encoder_lv4(images_lv4_2)
feature_lv4_3 = encoder_lv4(images_lv4_3)
feature_lv4_4 = encoder_lv4(images_lv4_4)
feature_lv4_5 = encoder_lv4(images_lv4_5)
feature_lv4_6 = encoder_lv4(images_lv4_6)
feature_lv4_7 = encoder_lv4(images_lv4_7)
feature_lv4_8 = encoder_lv4(images_lv4_8)
feature_lv4_top_left = torch.cat((feature_lv4_1, feature_lv4_2), 2)
feature_lv4_top_right = torch.cat((feature_lv4_3, feature_lv4_4),
2)
feature_lv4_bot_left = torch.cat((feature_lv4_5, feature_lv4_6), 2)
feature_lv4_bot_right = torch.cat((feature_lv4_7, feature_lv4_8),
2)
feature_lv4_top = torch.cat(
(feature_lv4_top_left, feature_lv4_top_right), 3)
feature_lv4_bot = torch.cat(
(feature_lv4_bot_left, feature_lv4_bot_right), 3)
feature_lv4 = torch.cat((feature_lv4_top, feature_lv4_bot), 2)
residual_lv4_top_left = decoder_lv4(feature_lv4_top_left)
residual_lv4_top_right = decoder_lv4(feature_lv4_top_right)
residual_lv4_bot_left = decoder_lv4(feature_lv4_bot_left)
residual_lv4_bot_right = decoder_lv4(feature_lv4_bot_right)
feature_lv3_1 = encoder_lv3(images_lv3_1 + residual_lv4_top_left)
feature_lv3_2 = encoder_lv3(images_lv3_2 + residual_lv4_top_right)
feature_lv3_3 = encoder_lv3(images_lv3_3 + residual_lv4_bot_left)
feature_lv3_4 = encoder_lv3(images_lv3_4 + residual_lv4_bot_right)
feature_lv3_top = torch.cat(
(feature_lv3_1, feature_lv3_2), 3) + feature_lv4_top
feature_lv3_bot = torch.cat(
(feature_lv3_3, feature_lv3_4), 3) + feature_lv4_bot
feature_lv3 = torch.cat((feature_lv3_top, feature_lv3_bot), 2)
residual_lv3_top = decoder_lv3(feature_lv3_top)
residual_lv3_bot = decoder_lv3(feature_lv3_bot)
feature_lv2_1 = encoder_lv2(images_lv2_1 + residual_lv3_top)
feature_lv2_2 = encoder_lv2(images_lv2_2 + residual_lv3_bot)
feature_lv2 = torch.cat(
(feature_lv2_1, feature_lv2_2), 2) + feature_lv3
residual_lv2 = decoder_lv2(feature_lv2)
feature_lv1 = encoder_lv1(images_lv1 + residual_lv2) + feature_lv2
deblur_image = decoder_lv1(feature_lv1)
loss = mse(deblur_image, gt)
encoder_lv1.zero_grad()
encoder_lv2.zero_grad()
encoder_lv3.zero_grad()
encoder_lv4.zero_grad()
decoder_lv1.zero_grad()
decoder_lv2.zero_grad()
decoder_lv3.zero_grad()
decoder_lv4.zero_grad()
loss.backward()
encoder_lv1_optim.step()
encoder_lv2_optim.step()
encoder_lv3_optim.step()
encoder_lv4_optim.step()
decoder_lv1_optim.step()
decoder_lv2_optim.step()
decoder_lv3_optim.step()
decoder_lv4_optim.step()
if (iteration + 1) % 10 == 0:
stop = time.time()
print(METHOD + " epoch:", epoch, "iteration:", iteration + 1,
"loss:%.4f" % loss.item(), 'time:%.4f' % (stop - start))
start = time.time()
encoder_lv1_scheduler.step(epoch)
encoder_lv2_scheduler.step(epoch)
encoder_lv3_scheduler.step(epoch)
encoder_lv4_scheduler.step(epoch)
decoder_lv1_scheduler.step(epoch)
decoder_lv2_scheduler.step(epoch)
decoder_lv3_scheduler.step(epoch)
decoder_lv4_scheduler.step(epoch)
if (epoch) % 100 == 0:
if os.path.exists('./checkpoints/' + METHOD + '/epoch' +
str(epoch)) == False:
os.system('mkdir ./checkpoints/' + METHOD + '/epoch' +
str(epoch))
print("Testing...")
test_dataset = GoProDataset(
blur_image_files='./datas/GoPro/test_blur_file.txt',
sharp_image_files='./datas/GoPro/test_sharp_file.txt',
root_dir='./datas/GoPro',
transform=transforms.Compose([transforms.ToTensor()]))
test_dataloader = DataLoader(test_dataset,
batch_size=1,
shuffle=False,
num_workers=8,
pin_memory=True)
test_time = 0
for iteration, images in enumerate(test_dataloader):
with torch.no_grad():
start = time.time()
images_lv1 = Variable(images['blur_image'] - 0.5).cuda(GPU)
H = images_lv1.size(2)
W = images_lv1.size(3)
images_lv2_1 = images_lv1[:, :, 0:int(H / 2), :]
images_lv2_2 = images_lv1[:, :, int(H / 2):H, :]
images_lv3_1 = images_lv2_1[:, :, :, 0:int(W / 2)]
images_lv3_2 = images_lv2_1[:, :, :, int(W / 2):W]
images_lv3_3 = images_lv2_2[:, :, :, 0:int(W / 2)]
images_lv3_4 = images_lv2_2[:, :, :, int(W / 2):W]
images_lv4_1 = images_lv3_1[:, :, 0:int(H / 4), :]
images_lv4_2 = images_lv3_1[:, :, int(H / 4):int(H / 2), :]
images_lv4_3 = images_lv3_2[:, :, 0:int(H / 4), :]
images_lv4_4 = images_lv3_2[:, :, int(H / 4):int(H / 2), :]
images_lv4_5 = images_lv3_3[:, :, 0:int(H / 4), :]
images_lv4_6 = images_lv3_3[:, :, int(H / 4):int(H / 2), :]
images_lv4_7 = images_lv3_4[:, :, 0:int(H / 4), :]
images_lv4_8 = images_lv3_4[:, :, int(H / 4):int(H / 2), :]
feature_lv4_1 = encoder_lv4(images_lv4_1)
feature_lv4_2 = encoder_lv4(images_lv4_2)
feature_lv4_3 = encoder_lv4(images_lv4_3)
feature_lv4_4 = encoder_lv4(images_lv4_4)
feature_lv4_5 = encoder_lv4(images_lv4_5)
feature_lv4_6 = encoder_lv4(images_lv4_6)
feature_lv4_7 = encoder_lv4(images_lv4_7)
feature_lv4_8 = encoder_lv4(images_lv4_8)
feature_lv4_top_left = torch.cat(
(feature_lv4_1, feature_lv4_2), 2)
feature_lv4_top_right = torch.cat(
(feature_lv4_3, feature_lv4_4), 2)
feature_lv4_bot_left = torch.cat(
(feature_lv4_5, feature_lv4_6), 2)
feature_lv4_bot_right = torch.cat(
(feature_lv4_7, feature_lv4_8), 2)
feature_lv4_top = torch.cat(
(feature_lv4_top_left, feature_lv4_top_right), 3)
feature_lv4_bot = torch.cat(
(feature_lv4_bot_left, feature_lv4_bot_right), 3)
residual_lv4_top_left = decoder_lv4(feature_lv4_top_left)
residual_lv4_top_right = decoder_lv4(feature_lv4_top_right)
residual_lv4_bot_left = decoder_lv4(feature_lv4_bot_left)
residual_lv4_bot_right = decoder_lv4(feature_lv4_bot_right)
feature_lv3_1 = encoder_lv3(images_lv3_1 +
residual_lv4_top_left)
feature_lv3_2 = encoder_lv3(images_lv3_2 +
residual_lv4_top_right)
feature_lv3_3 = encoder_lv3(images_lv3_3 +
residual_lv4_bot_left)
feature_lv3_4 = encoder_lv3(images_lv3_4 +
residual_lv4_bot_right)
feature_lv3_top = torch.cat(
(feature_lv3_1, feature_lv3_2), 3) + feature_lv4_top
feature_lv3_bot = torch.cat(
(feature_lv3_3, feature_lv3_4), 3) + feature_lv4_bot
residual_lv3_top = decoder_lv3(feature_lv3_top)
residual_lv3_bot = decoder_lv3(feature_lv3_bot)
feature_lv2_1 = encoder_lv2(images_lv2_1 +
residual_lv3_top)
feature_lv2_2 = encoder_lv2(images_lv2_2 +
residual_lv3_bot)
feature_lv2 = torch.cat(
(feature_lv2_1, feature_lv2_2), 2) + torch.cat(
(feature_lv3_top, feature_lv3_bot), 2)
residual_lv2 = decoder_lv2(feature_lv2)
feature_lv1 = encoder_lv1(images_lv1 +
residual_lv2) + feature_lv2
deblur_image = decoder_lv1(feature_lv1)
stop = time.time()
test_time += stop - start
print(
'RunTime:%.4f' % (stop - start),
' Average Runtime:%.4f' % (test_time /
(iteration + 1)))
save_deblur_images(deblur_image.data + 0.5, iteration,
epoch)
#
torch.save(
encoder_lv1.state_dict(),
str('./checkpoints/' + METHOD + '/epoch' + str(epoch) +
"/encoder_lv1.pkl"))
torch.save(
encoder_lv2.state_dict(),
str('./checkpoints/' + METHOD + '/epoch' + str(epoch) +
"/encoder_lv2.pkl"))
torch.save(
encoder_lv3.state_dict(),
str('./checkpoints/' + METHOD + '/epoch' + str(epoch) +
"/encoder_lv3.pkl"))
torch.save(
encoder_lv4.state_dict(),
str('./checkpoints/' + METHOD + '/epoch' + str(epoch) +
"/encoder_lv4.pkl"))
torch.save(
decoder_lv1.state_dict(),
str('./checkpoints/' + METHOD + '/epoch' + str(epoch) +
"/decoder_lv1.pkl"))
torch.save(
decoder_lv2.state_dict(),
str('./checkpoints/' + METHOD + '/epoch' + str(epoch) +
"/decoder_lv2.pkl"))
torch.save(
decoder_lv3.state_dict(),
str('./checkpoints/' + METHOD + '/epoch' + str(epoch) +
"/decoder_lv3.pkl"))
torch.save(
decoder_lv4.state_dict(),
str('./checkpoints/' + METHOD + '/epoch' + str(epoch) +
"/decoder_lv4.pkl"))
torch.save(encoder_lv1.state_dict(),
str('./checkpoints/' + METHOD + "/encoder_lv1.pkl"))
torch.save(encoder_lv2.state_dict(),
str('./checkpoints/' + METHOD + "/encoder_lv2.pkl"))
torch.save(encoder_lv3.state_dict(),
str('./checkpoints/' + METHOD + "/encoder_lv3.pkl"))
torch.save(encoder_lv4.state_dict(),
str('./checkpoints/' + METHOD + "/encoder_lv4.pkl"))
torch.save(decoder_lv1.state_dict(),
str('./checkpoints/' + METHOD + "/decoder_lv1.pkl"))
torch.save(decoder_lv2.state_dict(),
str('./checkpoints/' + METHOD + "/decoder_lv2.pkl"))
torch.save(decoder_lv3.state_dict(),
str('./checkpoints/' + METHOD + "/decoder_lv3.pkl"))
torch.save(decoder_lv4.state_dict(),
str('./checkpoints/' + METHOD + "/decoder_lv4.pkl"))
def main():
print("init data folders")
encoder_lv1 = models.Encoder().apply(weight_init).cuda(GPU)
encoder_lv2 = models.Encoder().apply(weight_init).cuda(GPU)
encoder_lv3 = models.Encoder().apply(weight_init).cuda(GPU)
encoder_lv4 = models.Encoder().apply(weight_init).cuda(GPU)
decoder_lv1 = models.Decoder().apply(weight_init).cuda(GPU)
decoder_lv2 = models.Decoder().apply(weight_init).cuda(GPU)
decoder_lv3 = models.Decoder().apply(weight_init).cuda(GPU)
decoder_lv4 = models.Decoder().apply(weight_init).cuda(GPU)
if os.path.exists(str('./checkpoints/' + METHOD + "/encoder_lv1.pkl")):
encoder_lv1.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/encoder_lv1.pkl")))
print("load encoder_lv1 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/encoder_lv2.pkl")):
encoder_lv2.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/encoder_lv2.pkl")))
print("load encoder_lv2 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/encoder_lv3.pkl")):
encoder_lv3.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/encoder_lv3.pkl")))
print("load encoder_lv3 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/encoder_lv4.pkl")):
encoder_lv4.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/encoder_lv4.pkl")))
print("load encoder_lv4 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/decoder_lv1.pkl")):
decoder_lv1.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/decoder_lv1.pkl")))
print("load encoder_lv1 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/decoder_lv2.pkl")):
decoder_lv2.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/decoder_lv2.pkl")))
print("load decoder_lv2 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/decoder_lv3.pkl")):
decoder_lv3.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/decoder_lv3.pkl")))
print("load decoder_lv3 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/decoder_lv4.pkl")):
decoder_lv4.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/decoder_lv4.pkl")))
print("load decoder_lv4 success")
if os.path.exists('./test_results/' + EXPDIR) == False:
os.system('mkdir ./test_results/' + EXPDIR)
# iteration = 0.0
# test_time = 0.0
print("Testing...")
test_dataset = GoProDataset(
blur_image_files='./datas/GoPro/test_blur_file.txt',
sharp_image_files='./datas/GoPro/test_sharp_file.txt',
root_dir='./datas/GoPro',
transform=transforms.Compose([
transforms.ToTensor()
]))
test_dataloader = DataLoader(test_dataset, batch_size=1, shuffle=False)
test_time = 0
for iteration, images in enumerate(test_dataloader):
with torch.no_grad():
start = time.time()
images_lv1 = Variable(images['blur_image'] - 0.5).cuda(GPU)
H = images_lv1.size(2)
W = images_lv1.size(3)
images_lv2_1 = images_lv1[:, :, 0:int(H / 2), :]
images_lv2_2 = images_lv1[:, :, int(H / 2):H, :]
images_lv3_1 = images_lv2_1[:, :, :, 0:int(W / 2)]
images_lv3_2 = images_lv2_1[:, :, :, int(W / 2):W]
images_lv3_3 = images_lv2_2[:, :, :, 0:int(W / 2)]
images_lv3_4 = images_lv2_2[:, :, :, int(W / 2):W]
images_lv4_1 = images_lv3_1[:, :, 0:int(H / 4), :]
images_lv4_2 = images_lv3_1[:, :, int(H / 4):int(H / 2), :]
images_lv4_3 = images_lv3_2[:, :, 0:int(H / 4), :]
images_lv4_4 = images_lv3_2[:, :, int(H / 4):int(H / 2), :]
images_lv4_5 = images_lv3_3[:, :, 0:int(H / 4), :]
images_lv4_6 = images_lv3_3[:, :, int(H / 4):int(H / 2), :]
images_lv4_7 = images_lv3_4[:, :, 0:int(H / 4), :]
images_lv4_8 = images_lv3_4[:, :, int(H / 4):int(H / 2), :]
feature_lv4_1 = encoder_lv4(images_lv4_1)
feature_lv4_2 = encoder_lv4(images_lv4_2)
feature_lv4_3 = encoder_lv4(images_lv4_3)
feature_lv4_4 = encoder_lv4(images_lv4_4)
feature_lv4_5 = encoder_lv4(images_lv4_5)
feature_lv4_6 = encoder_lv4(images_lv4_6)
feature_lv4_7 = encoder_lv4(images_lv4_7)
feature_lv4_8 = encoder_lv4(images_lv4_8)
feature_lv4_top_left = torch.cat((feature_lv4_1, feature_lv4_2), 2)
feature_lv4_top_right = torch.cat((feature_lv4_3, feature_lv4_4), 2)
feature_lv4_bot_left = torch.cat((feature_lv4_5, feature_lv4_6), 2)
feature_lv4_bot_right = torch.cat((feature_lv4_7, feature_lv4_8), 2)
feature_lv4_top = torch.cat((feature_lv4_top_left, feature_lv4_top_right), 3)
feature_lv4_bot = torch.cat((feature_lv4_bot_left, feature_lv4_bot_right), 3)
residual_lv4_top_left = decoder_lv4(feature_lv4_top_left)
residual_lv4_top_right = decoder_lv4(feature_lv4_top_right)
residual_lv4_bot_left = decoder_lv4(feature_lv4_bot_left)
residual_lv4_bot_right = decoder_lv4(feature_lv4_bot_right)
# output residual of level4
feature_lv4 = torch.cat((feature_lv4_top, feature_lv4_bot), 2)
residual_lv4 = decoder_lv4(feature_lv4)
feature_lv3_1 = encoder_lv3(images_lv3_1 + residual_lv4_top_left)
feature_lv3_2 = encoder_lv3(images_lv3_2 + residual_lv4_top_right)
feature_lv3_3 = encoder_lv3(images_lv3_3 + residual_lv4_bot_left)
feature_lv3_4 = encoder_lv3(images_lv3_4 + residual_lv4_bot_right)
feature_lv3_top = torch.cat((feature_lv3_1, feature_lv3_2), 3) + feature_lv4_top
feature_lv3_bot = torch.cat((feature_lv3_3, feature_lv3_4), 3) + feature_lv4_bot
residual_lv3_top = decoder_lv3(feature_lv3_top)
residual_lv3_bot = decoder_lv3(feature_lv3_bot)
# output residual of level3
feature_lv3 = torch.cat((feature_lv3_top, feature_lv3_bot), 2)
residual_lv3 = decoder_lv3(feature_lv3)
feature_lv2_1 = encoder_lv2(images_lv2_1 + residual_lv3_top)
feature_lv2_2 = encoder_lv2(images_lv2_2 + residual_lv3_bot)
feature_lv2 = torch.cat((feature_lv2_1, feature_lv2_2), 2) + torch.cat((feature_lv3_top, feature_lv3_bot),
2)
residual_lv2 = decoder_lv2(feature_lv2)
feature_lv1 = encoder_lv1(images_lv1 + residual_lv2) + feature_lv2
deblur_image = decoder_lv1(feature_lv1)
stop = time.time()
test_time += stop - start
print('RunTime:%.4f' % (stop - start), ' Average Runtime:%.4f' % (test_time / (iteration + 1)))
# 保存特征图，查看
save_residual_images(residual_lv4.data, iteration, "residual_lv4")
save_residual_images(residual_lv3.data, iteration, "residual_lv3")
save_residual_images(residual_lv2.data, iteration, "residual_lv2")
# save_residual_images(feature_lv1, iteration, "feature_lv1")
save_images(deblur_image.data + 0.5, iteration)
def main():
print("init data folders")
torch.backends.cudnn.benchmark = True
torch.cuda.empty_cache()
encoder = {}
decoder = {}
encoder_optim = {}
decoder_optim = {}
encoder_scheduler = {}
decoder_scheduler = {}
for s in ['s1', 's2', 's3', 's4']:
encoder[s] = {}
decoder[s] = {}
encoder_optim[s] = {}
decoder_optim[s] = {}
encoder_scheduler[s] = {}
decoder_scheduler[s] = {}
for lv in ['lv1', 'lv2', 'lv3']:
encoder[s][lv] = models.Encoder()
decoder[s][lv] = models.Decoder()
encoder[s][lv].apply(weight_init).cuda(GPU)
decoder[s][lv].apply(weight_init).cuda(GPU)
encoder_optim[s][lv] = RAdam(encoder[s][lv].parameters(),
lr=LEARNING_RATE)
decoder_optim[s][lv] = RAdam(decoder[s][lv].parameters(),
lr=LEARNING_RATE)
encoder_scheduler[s][lv] = StepLR(encoder_optim[s][lv],
step_size=1000,
gamma=0.1)
decoder_scheduler[s][lv] = StepLR(decoder_optim[s][lv],
step_size=1000,
gamma=0.1)
if os.path.exists(
str('./checkpoints/' + METHOD + "/encoder_" + s + "_" +
lv + ".pkl")):
encoder[s][lv].load_state_dict(
torch.load(
str('./checkpoints/' + METHOD + "/encoder_" + s + "_" +
lv + ".pkl")))
print("load encoder_" + s + "_" + lv + " successfully!")
if os.path.exists(
str('./checkpoints/' + METHOD + "/decoder_" + s + "_" +
lv + ".pkl")):
decoder[s][lv].load_state_dict(
torch.load(
str('./checkpoints/' + METHOD + "/decoder_" + s + "_" +
lv + ".pkl")))
print("load decoder_" + s + "_" + lv + " successfully!")
if os.path.exists('./checkpoints/' + METHOD) == False:
os.system('mkdir ./checkpoints/' + METHOD)
for epoch in range(args.start_epoch, EPOCHS):
torch.cuda.empty_cache()
print("Training...")
print('lr:', encoder_scheduler[s][lv].get_lr())
train_dataset = GoProDataset(
blur_image_files='./datas/GoPro/train_blur_file.txt',
sharp_image_files='./datas/GoPro/train_sharp_file.txt',
root_dir='./datas/GoPro/',
crop=True,
crop_size=CROP_SIZE,
rotation=True,
color_augment=True,
transform=transforms.Compose([transforms.ToTensor()]))
train_dataloader = DataLoader(train_dataset,
batch_size=BATCH_SIZE,
shuffle=True,
num_workers=16,
pin_memory=True)
start = 0
for iteration, inputs in enumerate(train_dataloader):
mse = nn.MSELoss().cuda(GPU)
images = {}
feature = {}
residual = {}
for s in ['s1', 's2', 's3', 's4']:
feature[s] = {}
residual[s] = {}
images['gt'] = Variable(inputs['sharp_image'] - 0.5).cuda(GPU)
images['lv1'] = Variable(inputs['blur_image'] - 0.5).cuda(GPU)
H = images['lv1'].size(2)
W = images['lv1'].size(3)
images['lv2_1'] = images['lv1'][:, :, 0:int(H / 2), :]
images['lv2_2'] = images['lv1'][:, :, int(H / 2):H, :]
images['lv3_1'] = images['lv2_1'][:, :, :, 0:int(W / 2)]
images['lv3_2'] = images['lv2_1'][:, :, :, int(W / 2):W]
images['lv3_3'] = images['lv2_2'][:, :, :, 0:int(W / 2)]
images['lv3_4'] = images['lv2_2'][:, :, :, int(W / 2):W]
s = 's1'
feature[s]['lv3_1'] = encoder[s]['lv3'](images['lv3_1'])
feature[s]['lv3_2'] = encoder[s]['lv3'](images['lv3_2'])
feature[s]['lv3_3'] = encoder[s]['lv3'](images['lv3_3'])
feature[s]['lv3_4'] = encoder[s]['lv3'](images['lv3_4'])
feature[s]['lv3_top'] = torch.cat(
(feature[s]['lv3_1'], feature[s]['lv3_2']), 3)
feature[s]['lv3_bot'] = torch.cat(
(feature[s]['lv3_3'], feature[s]['lv3_4']), 3)
residual[s]['lv3_top'] = decoder[s]['lv3'](feature[s]['lv3_top'])
residual[s]['lv3_bot'] = decoder[s]['lv3'](feature[s]['lv3_bot'])
feature[s]['lv2_1'] = encoder[s]['lv2'](
images['lv2_1'] +
residual[s]['lv3_top']) + feature[s]['lv3_top']
feature[s]['lv2_2'] = encoder[s]['lv2'](
images['lv2_2'] +
residual[s]['lv3_bot']) + feature[s]['lv3_bot']
feature[s]['lv2'] = torch.cat(
(feature[s]['lv2_1'], feature[s]['lv2_2']), 2)
residual[s]['lv2'] = decoder[s]['lv2'](feature[s]['lv2'])
feature[s]['lv1'] = encoder[s]['lv1'](
images['lv1'] + residual[s]['lv2']) + feature[s]['lv2']
residual[s]['lv1'] = decoder[s]['lv1'](feature[s]['lv1'])
s = 's2'
ps = 's1'
feature[s]['lv3_1'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, 0:int(H / 2), 0:int(W / 2)])
feature[s]['lv3_2'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, 0:int(H / 2),
int(W / 2):W])
feature[s]['lv3_3'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, int(H / 2):H, 0:int(W / 2)])
feature[s]['lv3_4'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, int(H / 2):H,
int(W / 2):W])
feature[s]['lv3_top'] = torch.cat(
(feature[s]['lv3_1'], feature[s]['lv3_2']),
3) + feature[ps]['lv3_top']
feature[s]['lv3_bot'] = torch.cat(
(feature[s]['lv3_3'], feature[s]['lv3_4']),
3) + feature[ps]['lv3_bot']
residual[s]['lv3_top'] = decoder[s]['lv3'](feature[s]['lv3_top'])
residual[s]['lv3_bot'] = decoder[s]['lv3'](feature[s]['lv3_bot'])
feature[s]['lv2_1'] = encoder[s]['lv2'](
residual[ps]['lv1'][:, :, 0:int(H / 2), :] + residual[s]
['lv3_top']) + feature[s]['lv3_top'] + feature[ps]['lv2_1']
feature[s]['lv2_2'] = encoder[s]['lv2'](
residual[ps]['lv1'][:, :, int(H / 2):H, :] + residual[s]
['lv3_bot']) + feature[s]['lv3_bot'] + feature[ps]['lv2_2']
feature[s]['lv2'] = torch.cat(
(feature[s]['lv2_1'], feature[s]['lv2_2']), 2)
residual[s]['lv2'] = decoder[s]['lv2'](
feature[s]['lv2']) + residual['s1']['lv1']
feature[s]['lv1'] = encoder[s]['lv1'](
residual[ps]['lv1'] +
residual[s]['lv2']) + feature[s]['lv2'] + feature[ps]['lv1']
residual[s]['lv1'] = decoder[s]['lv1'](feature[s]['lv1'])
s = 's3'
ps = 's2'
feature[s]['lv3_1'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, 0:int(H / 2), 0:int(W / 2)])
feature[s]['lv3_2'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, 0:int(H / 2),
int(W / 2):W])
feature[s]['lv3_3'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, int(H / 2):H, 0:int(W / 2)])
feature[s]['lv3_4'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, int(H / 2):H,
int(W / 2):W])
feature[s]['lv3_top'] = torch.cat(
(feature[s]['lv3_1'], feature[s]['lv3_2']),
3) + feature[ps]['lv3_top']
feature[s]['lv3_bot'] = torch.cat(
(feature[s]['lv3_3'], feature[s]['lv3_4']),
3) + feature[ps]['lv3_bot']
residual[s]['lv3_top'] = decoder[s]['lv3'](feature[s]['lv3_top'])
residual[s]['lv3_bot'] = decoder[s]['lv3'](feature[s]['lv3_bot'])
feature[s]['lv2_1'] = encoder[s]['lv2'](
residual[ps]['lv1'][:, :, 0:int(H / 2), :] + residual[s]
['lv3_top']) + feature[s]['lv3_top'] + feature[ps]['lv2_1']
feature[s]['lv2_2'] = encoder[s]['lv2'](
residual[ps]['lv1'][:, :, int(H / 2):H, :] + residual[s]
['lv3_bot']) + feature[s]['lv3_bot'] + feature[ps]['lv2_2']
feature[s]['lv2'] = torch.cat(
(feature[s]['lv2_1'], feature[s]['lv2_2']), 2)
residual[s]['lv2'] = decoder[s]['lv2'](
feature[s]['lv2']) + residual['s1']['lv1']
feature[s]['lv1'] = encoder[s]['lv1'](
residual[ps]['lv1'] +
residual[s]['lv2']) + feature[s]['lv2'] + feature[ps]['lv1']
residual[s]['lv1'] = decoder[s]['lv1'](feature[s]['lv1'])
s = 's4'
ps = 's3'
feature[s]['lv3_1'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, 0:int(H / 2), 0:int(W / 2)])
feature[s]['lv3_2'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, 0:int(H / 2),
int(W / 2):W])
feature[s]['lv3_3'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, int(H / 2):H, 0:int(W / 2)])
feature[s]['lv3_4'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, int(H / 2):H,
int(W / 2):W])
feature[s]['lv3_top'] = torch.cat(
(feature[s]['lv3_1'], feature[s]['lv3_2']),
3) + feature[ps]['lv3_top']
feature[s]['lv3_bot'] = torch.cat(
(feature[s]['lv3_3'], feature[s]['lv3_4']),
3) + feature[ps]['lv3_bot']
residual[s]['lv3_top'] = decoder[s]['lv3'](feature[s]['lv3_top'])
residual[s]['lv3_bot'] = decoder[s]['lv3'](feature[s]['lv3_bot'])
feature[s]['lv2_1'] = encoder[s]['lv2'](
residual[ps]['lv1'][:, :, 0:int(H / 2), :] + residual[s]
['lv3_top']) + feature[s]['lv3_top'] + feature[ps]['lv2_1']
feature[s]['lv2_2'] = encoder[s]['lv2'](
residual[ps]['lv1'][:, :, int(H / 2):H, :] + residual[s]
['lv3_bot']) + feature[s]['lv3_bot'] + feature[ps]['lv2_2']
feature[s]['lv2'] = torch.cat(
(feature[s]['lv2_1'], feature[s]['lv2_2']), 2)
residual[s]['lv2'] = decoder[s]['lv2'](
feature[s]['lv2']) + residual['s1']['lv1']
feature[s]['lv1'] = encoder[s]['lv1'](
residual[ps]['lv1'] +
residual[s]['lv2']) + feature[s]['lv2'] + feature[ps]['lv1']
residual[s]['lv1'] = decoder[s]['lv1'](feature[s]['lv1'])
loss = mse(residual['s4']['lv1'], images['gt']) + mse(
residual['s3']['lv1'], images['gt']) + mse(
residual['s2']['lv1'], images['gt']) + mse(
residual['s1']['lv1'], images['gt'])
for s in ['s1', 's2', 's3', 's4']:
for lv in ['lv1', 'lv2', 'lv3']:
encoder[s][lv].zero_grad()
decoder[s][lv].zero_grad()
loss.backward()
for s in ['s1', 's2', 's3', 's4']:
for lv in ['lv1', 'lv2', 'lv3']:
encoder_optim[s][lv].step()
decoder_optim[s][lv].step()
if (iteration + 1) % 10 == 0:
stop = time.time()
print(METHOD + " epoch:", epoch, "iteration:", iteration + 1,
"loss:%.4f" % loss.item(), 'time:%.4f' % (stop - start))
start = time.time()
for s in ['s1', 's2', 's3', 's4']:
for lv in ['lv1', 'lv2', 'lv3']:
encoder_scheduler[s][lv].step(epoch)
decoder_scheduler[s][lv].step(epoch)
if (epoch) % 100 == 0:
if os.path.exists('./checkpoints/' + METHOD + '/epoch' +
str(epoch)) == False:
os.system('mkdir ./checkpoints/' + METHOD + '/epoch' +
str(epoch))
for s in ['s1', 's2', 's3', 's4']:
for lv in ['lv1', 'lv2', 'lv3']:
torch.save(
encoder[s][lv].state_dict(),
str('./checkpoints/' + METHOD + '/epoch' + str(epoch) +
"/encoder_" + s + "_" + lv + ".pkl"))
torch.save(
decoder[s][lv].state_dict(),
str('./checkpoints/' + METHOD + '/epoch' + str(epoch) +
"/decoder_" + s + "_" + lv + ".pkl"))
print("Testing...")
test_dataset = GoProDataset(
blur_image_files='./datas/GoPro/test_blur_file.txt',
sharp_image_files='./datas/GoPro/test_sharp_file.txt',
root_dir='./datas/GoPro/',
transform=transforms.Compose([transforms.ToTensor()]))
test_dataloader = DataLoader(test_dataset,
batch_size=1,
shuffle=False,
num_workers=16,
pin_memory=True)
for iteration, inputs in enumerate(test_dataloader):
with torch.no_grad():
images['lv1'] = Variable(inputs['blur_image'] -
0.5).cuda(GPU)
H = images['lv1'].size(2)
W = images['lv1'].size(3)
images['lv2_1'] = images['lv1'][:, :, 0:int(H / 2), :]
images['lv2_2'] = images['lv1'][:, :, int(H / 2):H, :]
images['lv3_1'] = images['lv2_1'][:, :, :, 0:int(W / 2)]
images['lv3_2'] = images['lv2_1'][:, :, :, int(W / 2):W]
images['lv3_3'] = images['lv2_2'][:, :, :, 0:int(W / 2)]
images['lv3_4'] = images['lv2_2'][:, :, :, int(W / 2):W]
s = 's1'
feature[s]['lv3_1'] = encoder[s]['lv3'](images['lv3_1'])
feature[s]['lv3_2'] = encoder[s]['lv3'](images['lv3_2'])
feature[s]['lv3_3'] = encoder[s]['lv3'](images['lv3_3'])
feature[s]['lv3_4'] = encoder[s]['lv3'](images['lv3_4'])
feature[s]['lv3_top'] = torch.cat(
(feature[s]['lv3_1'], feature[s]['lv3_2']), 3)
feature[s]['lv3_bot'] = torch.cat(
(feature[s]['lv3_3'], feature[s]['lv3_4']), 3)
residual[s]['lv3_top'] = decoder[s]['lv3'](
feature[s]['lv3_top'])
residual[s]['lv3_bot'] = decoder[s]['lv3'](
feature[s]['lv3_bot'])
feature[s]['lv2_1'] = encoder[s]['lv2'](
images['lv2_1'] +
residual[s]['lv3_top']) + feature[s]['lv3_top']
feature[s]['lv2_2'] = encoder[s]['lv2'](
images['lv2_2'] +
residual[s]['lv3_bot']) + feature[s]['lv3_bot']
feature[s]['lv2'] = torch.cat(
(feature[s]['lv2_1'], feature[s]['lv2_2']), 2)
residual[s]['lv2'] = decoder[s]['lv2'](feature[s]['lv2'])
feature[s]['lv1'] = encoder[s]['lv1'](
images['lv1'] + residual[s]['lv2']) + feature[s]['lv2']
residual[s]['lv1'] = decoder[s]['lv1'](feature[s]['lv1'])
s = 's2'
ps = 's1'
feature[s]['lv3_1'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, 0:int(H / 2), 0:int(W / 2)])
feature[s]['lv3_2'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, 0:int(H / 2),
int(W / 2):W])
feature[s]['lv3_3'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :,
int(H / 2):H, 0:int(W / 2)])
feature[s]['lv3_4'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :,
int(H / 2):H,
int(W / 2):W])
feature[s]['lv3_top'] = torch.cat(
(feature[s]['lv3_1'], feature[s]['lv3_2']),
3) + feature[ps]['lv3_top']
feature[s]['lv3_bot'] = torch.cat(
(feature[s]['lv3_3'], feature[s]['lv3_4']),
3) + feature[ps]['lv3_bot']
residual[s]['lv3_top'] = decoder[s]['lv3'](
feature[s]['lv3_top'])
residual[s]['lv3_bot'] = decoder[s]['lv3'](
feature[s]['lv3_bot'])
feature[s]['lv2_1'] = encoder[s]['lv2'](
residual[ps]['lv1'][:, :, 0:int(H / 2), :] +
residual[s]['lv3_top']
) + feature[s]['lv3_top'] + feature[ps]['lv2_1']
feature[s]['lv2_2'] = encoder[s]['lv2'](
residual[ps]['lv1'][:, :, int(H / 2):H, :] +
residual[s]['lv3_bot']
) + feature[s]['lv3_bot'] + feature[ps]['lv2_2']
feature[s]['lv2'] = torch.cat(
(feature[s]['lv2_1'], feature[s]['lv2_2']), 2)
residual[s]['lv2'] = decoder[s]['lv2'](feature[s]['lv2'])
feature[s]['lv1'] = encoder[s]['lv1'](
residual[ps]['lv1'] + residual[s]['lv2']
) + feature[s]['lv2'] + feature[ps]['lv1']
residual[s]['lv1'] = decoder[s]['lv1'](feature[s]['lv1'])
s = 's3'
ps = 's2'
feature[s]['lv3_1'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, 0:int(H / 2), 0:int(W / 2)])
feature[s]['lv3_2'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, 0:int(H / 2),
int(W / 2):W])
feature[s]['lv3_3'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :,
int(H / 2):H, 0:int(W / 2)])
feature[s]['lv3_4'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :,
int(H / 2):H,
int(W / 2):W])
feature[s]['lv3_top'] = torch.cat(
(feature[s]['lv3_1'], feature[s]['lv3_2']),
3) + feature[ps]['lv3_top']
feature[s]['lv3_bot'] = torch.cat(
(feature[s]['lv3_3'], feature[s]['lv3_4']),
3) + feature[ps]['lv3_bot']
residual[s]['lv3_top'] = decoder[s]['lv3'](
feature[s]['lv3_top'])
residual[s]['lv3_bot'] = decoder[s]['lv3'](
feature[s]['lv3_bot'])
feature[s]['lv2_1'] = encoder[s]['lv2'](
residual[ps]['lv1'][:, :, 0:int(H / 2), :] +
residual[s]['lv3_top']
) + feature[s]['lv3_top'] + feature[ps]['lv2_1']
feature[s]['lv2_2'] = encoder[s]['lv2'](
residual[ps]['lv1'][:, :, int(H / 2):H, :] +
residual[s]['lv3_bot']
) + feature[s]['lv3_bot'] + feature[ps]['lv2_2']
feature[s]['lv2'] = torch.cat(
(feature[s]['lv2_1'], feature[s]['lv2_2']), 2)
residual[s]['lv2'] = decoder[s]['lv2'](feature[s]['lv2'])
feature[s]['lv1'] = encoder[s]['lv1'](
residual[ps]['lv1'] + residual[s]['lv2']
) + feature[s]['lv2'] + feature[ps]['lv1']
residual[s]['lv1'] = decoder[s]['lv1'](feature[s]['lv1'])
s = 's4'
ps = 's3'
feature[s]['lv3_1'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, 0:int(H / 2), 0:int(W / 2)])
feature[s]['lv3_2'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :, 0:int(H / 2),
int(W / 2):W])
feature[s]['lv3_3'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :,
int(H / 2):H, 0:int(W / 2)])
feature[s]['lv3_4'] = encoder[s]['lv3'](
residual[ps]['lv1'][:, :,
int(H / 2):H,
int(W / 2):W])
feature[s]['lv3_top'] = torch.cat(
(feature[s]['lv3_1'], feature[s]['lv3_2']),
3) + feature[ps]['lv3_top']
feature[s]['lv3_bot'] = torch.cat(
(feature[s]['lv3_3'], feature[s]['lv3_4']),
3) + feature[ps]['lv3_bot']
residual[s]['lv3_top'] = decoder[s]['lv3'](
feature[s]['lv3_top'])
residual[s]['lv3_bot'] = decoder[s]['lv3'](
feature[s]['lv3_bot'])
feature[s]['lv2_1'] = encoder[s]['lv2'](
residual[ps]['lv1'][:, :, 0:int(H / 2), :] +
residual[s]['lv3_top']
) + feature[s]['lv3_top'] + feature[ps]['lv2_1']
feature[s]['lv2_2'] = encoder[s]['lv2'](
residual[ps]['lv1'][:, :, int(H / 2):H, :] +
residual[s]['lv3_bot']
) + feature[s]['lv3_bot'] + feature[ps]['lv2_2']
feature[s]['lv2'] = torch.cat(
(feature[s]['lv2_1'], feature[s]['lv2_2']), 2)
residual[s]['lv2'] = decoder[s]['lv2'](feature[s]['lv2'])
feature[s]['lv1'] = encoder[s]['lv1'](
residual[ps]['lv1'] + residual[s]['lv2']
) + feature[s]['lv2'] + feature[ps]['lv1']
residual[s]['lv1'] = decoder[s]['lv1'](feature[s]['lv1'])
deblurred_image = residual[s]['lv1']
save_deblur_images(deblurred_image.data + 0.5, iteration,
epoch)
for s in ['s1', 's2', 's3', 's4']:
for lv in ['lv1', 'lv2', 'lv3']:
torch.save(
encoder[s][lv].state_dict(),
str('./checkpoints/' + METHOD + '/epoch' +
str(epoch) + "/encoder_" + s + "_" + lv +
".pkl"))
torch.save(
decoder[s][lv].state_dict(),
str('./checkpoints/' + METHOD + '/epoch' +
str(epoch) + "/decoder_" + s + "_" + lv +
".pkl"))
for s in ['s1', 's2', 's3', 's4']:
for lv in ['lv1', 'lv2', 'lv3']:
torch.save(
encoder[s][lv].state_dict(),
str('./checkpoints/' + METHOD + "/encoder_" + s + "_" +
lv + ".pkl"))
torch.save(
decoder[s][lv].state_dict(),
str('./checkpoints/' + METHOD + "/decoder_" + s + "_" +
lv + ".pkl"))
def main():
print("init data folders")
psnr_list = []
encoder_lv1 = models.Encoder()
encoder_lv2 = models.Encoder()
encoder_lv3 = models.Encoder()
decoder_lv1 = models.Decoder()
decoder_lv2 = models.Decoder()
decoder_lv3 = models.Decoder()
encoder_lv1.apply(weight_init).cuda(GPU)
encoder_lv2.apply(weight_init).cuda(GPU)
encoder_lv3.apply(weight_init).cuda(GPU)
decoder_lv1.apply(weight_init).cuda(GPU)
decoder_lv2.apply(weight_init).cuda(GPU)
decoder_lv3.apply(weight_init).cuda(GPU)
encoder_lv1_optim = torch.optim.Adam(encoder_lv1.parameters(),
lr=LEARNING_RATE)
encoder_lv1_scheduler = StepLR(encoder_lv1_optim,
step_size=1000,
gamma=0.1)
encoder_lv2_optim = torch.optim.Adam(encoder_lv2.parameters(),
lr=LEARNING_RATE)
encoder_lv2_scheduler = StepLR(encoder_lv2_optim,
step_size=1000,
gamma=0.1)
encoder_lv3_optim = torch.optim.Adam(encoder_lv3.parameters(),
lr=LEARNING_RATE)
encoder_lv3_scheduler = StepLR(encoder_lv3_optim,
step_size=1000,
gamma=0.1)
decoder_lv1_optim = torch.optim.Adam(decoder_lv1.parameters(),
lr=LEARNING_RATE)
decoder_lv1_scheduler = StepLR(decoder_lv1_optim,
step_size=1000,
gamma=0.1)
decoder_lv2_optim = torch.optim.Adam(decoder_lv2.parameters(),
lr=LEARNING_RATE)
decoder_lv2_scheduler = StepLR(decoder_lv2_optim,
step_size=1000,
gamma=0.1)
decoder_lv3_optim = torch.optim.Adam(decoder_lv3.parameters(),
lr=LEARNING_RATE)
decoder_lv3_scheduler = StepLR(decoder_lv3_optim,
step_size=1000,
gamma=0.1)
if os.path.exists(str('./checkpoints/' + METHOD + "/encoder_lv1.pkl")):
encoder_lv1.load_state_dict(
torch.load(str('./checkpoints/' + METHOD + "/encoder_lv1.pkl")))
print("load encoder_lv1 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/encoder_lv2.pkl")):
encoder_lv2.load_state_dict(
torch.load(str('./checkpoints/' + METHOD + "/encoder_lv2.pkl")))
print("load encoder_lv2 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/encoder_lv3.pkl")):
encoder_lv3.load_state_dict(
torch.load(str('./checkpoints/' + METHOD + "/encoder_lv3.pkl")))
print("load encoder_lv3 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/decoder_lv1.pkl")):
decoder_lv1.load_state_dict(
torch.load(str('./checkpoints/' + METHOD + "/decoder_lv1.pkl")))
print("load encoder_lv1 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/decoder_lv2.pkl")):
decoder_lv2.load_state_dict(
torch.load(str('./checkpoints/' + METHOD + "/decoder_lv2.pkl")))
print("load decoder_lv2 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/decoder_lv3.pkl")):
decoder_lv3.load_state_dict(
torch.load(str('./checkpoints/' + METHOD + "/decoder_lv3.pkl")))
print("load decoder_lv3 success")
if os.path.exists('./checkpoints/' + METHOD) == False:
os.system('mkdir ./checkpoints/' + METHOD)
for epoch in range(args.start_epoch, EPOCHS):
encoder_lv1_scheduler.step(epoch)
encoder_lv2_scheduler.step(epoch)
encoder_lv3_scheduler.step(epoch)
decoder_lv1_scheduler.step(epoch)
decoder_lv2_scheduler.step(epoch)
decoder_lv3_scheduler.step(epoch)
print("Training...")
train_dataset = GoProDataset(
blur_image_files='./datas/GoPro/train_blur_file.txt',
sharp_image_files='./datas/GoPro/train_sharp_file.txt',
root_dir='./datas/GoPro/',
crop=True,
crop_size=IMAGE_SIZE,
transform=transforms.Compose([transforms.ToTensor()]))
train_dataloader = DataLoader(train_dataset,
batch_size=BATCH_SIZE,
shuffle=True)
start = 0
for iteration, images in enumerate(train_dataloader):
mse = nn.MSELoss().cuda(GPU)
gt = Variable(images['sharp_image'] - 0.5).cuda(GPU)
H = gt.size(2)
W = gt.size(3)
images_lv1 = Variable(images['blur_image'] - 0.5).cuda(GPU)
images_lv2_1 = images_lv1[:, :, 0:int(H / 2), :]
images_lv2_2 = images_lv1[:, :, int(H / 2):H, :]
images_lv3_1 = images_lv2_1[:, :, :, 0:int(W / 2)]
images_lv3_2 = images_lv2_1[:, :, :, int(W / 2):W]
images_lv3_3 = images_lv2_2[:, :, :, 0:int(W / 2)]
images_lv3_4 = images_lv2_2[:, :, :, int(W / 2):W]
feature_lv3_1 = encoder_lv3(images_lv3_1)
feature_lv3_2 = encoder_lv3(images_lv3_2)
feature_lv3_3 = encoder_lv3(images_lv3_3)
feature_lv3_4 = encoder_lv3(images_lv3_4)
feature_lv3_top = torch.cat((feature_lv3_1, feature_lv3_2), 3)
feature_lv3_bot = torch.cat((feature_lv3_3, feature_lv3_4), 3)
feature_lv3 = torch.cat((feature_lv3_top, feature_lv3_bot), 2)
residual_lv3_top = decoder_lv3(feature_lv3_top)
residual_lv3_bot = decoder_lv3(feature_lv3_bot)
feature_lv2_1 = encoder_lv2(images_lv2_1 + residual_lv3_top)
feature_lv2_2 = encoder_lv2(images_lv2_2 + residual_lv3_bot)
feature_lv2 = torch.cat(
(feature_lv2_1, feature_lv2_2), 2) + feature_lv3
residual_lv2 = decoder_lv2(feature_lv2)
feature_lv1 = encoder_lv1(images_lv1 + residual_lv2) + feature_lv2
deblur_image = decoder_lv1(feature_lv1)
loss_lv1 = mse(deblur_image, gt)
loss = loss_lv1
encoder_lv1.zero_grad()
encoder_lv2.zero_grad()
encoder_lv3.zero_grad()
decoder_lv1.zero_grad()
decoder_lv2.zero_grad()
decoder_lv3.zero_grad()
loss.backward()
encoder_lv1_optim.step()
encoder_lv2_optim.step()
encoder_lv3_optim.step()
decoder_lv1_optim.step()
decoder_lv2_optim.step()
decoder_lv3_optim.step()
if (iteration + 1) % 50 == 0:
stop = time.time()
print("epoch:", epoch, "iteration:", iteration + 1,
"loss:%.4f" % loss.item(), 'time:%.4f' % (stop - start))
start = time.time()
if (epoch) % 100 == 0:
if os.path.exists('./checkpoints/' + METHOD + '/epoch' +
str(epoch)) == False:
os.system('mkdir ./checkpoints/' + METHOD + '/epoch' +
str(epoch))
print("Testing...")
test_dataset = GoProDataset(
blur_image_files='./datas/GoPro/test_blur_file.txt',
sharp_image_files='./datas/GoPro/test_sharp_file.txt',
root_dir='./datas/GoPro/',
transform=transforms.Compose([transforms.ToTensor()]))
test_dataloader = DataLoader(test_dataset,
batch_size=1,
shuffle=False)
test_time = 0.0
total_psnr = 0
for iteration, images in enumerate(test_dataloader):
with torch.no_grad():
images_lv1 = Variable(images['blur_image'] - 0.5).cuda(GPU)
start = time.time()
H = images_lv1.size(2)
W = images_lv1.size(3)
images_lv2_1 = images_lv1[:, :, 0:int(H / 2), :]
images_lv2_2 = images_lv1[:, :, int(H / 2):H, :]
images_lv3_1 = images_lv2_1[:, :, :, 0:int(W / 2)]
images_lv3_2 = images_lv2_1[:, :, :, int(W / 2):W]
images_lv3_3 = images_lv2_2[:, :, :, 0:int(W / 2)]
images_lv3_4 = images_lv2_2[:, :, :, int(W / 2):W]
feature_lv3_1 = encoder_lv3(images_lv3_1)
feature_lv3_2 = encoder_lv3(images_lv3_2)
feature_lv3_3 = encoder_lv3(images_lv3_3)
feature_lv3_4 = encoder_lv3(images_lv3_4)
feature_lv3_top = torch.cat((feature_lv3_1, feature_lv3_2),
3)
feature_lv3_bot = torch.cat((feature_lv3_3, feature_lv3_4),
3)
feature_lv3 = torch.cat((feature_lv3_top, feature_lv3_bot),
2)
residual_lv3_top = decoder_lv3(feature_lv3_top)
residual_lv3_bot = decoder_lv3(feature_lv3_bot)
feature_lv2_1 = encoder_lv2(images_lv2_1 +
residual_lv3_top)
feature_lv2_2 = encoder_lv2(images_lv2_2 +
residual_lv3_bot)
feature_lv2 = torch.cat(
(feature_lv2_1, feature_lv2_2), 2) + feature_lv3
residual_lv2 = decoder_lv2(feature_lv2)
feature_lv1 = encoder_lv1(images_lv1 +
residual_lv2) + feature_lv2
deblur_image = decoder_lv1(feature_lv1)
stop = time.time()
test_time += stop - start
psnr = compare_psnr(
images['sharp_image'].numpy()[0],
deblur_image.detach().cpu().numpy()[0] + 0.5)
total_psnr += psnr
if (iteration + 1) % 50 == 0:
print(
'PSNR:%.4f' % (psnr), ' Average PSNR:%.4f' %
(total_psnr / (iteration + 1)))
save_deblur_images(deblur_image.data + 0.5, iteration,
epoch)
psnr_list.append(total_psnr / (iteration + 1))
print("PSNR list:")
print(psnr_list)
torch.save(encoder_lv1.state_dict(),
str('./checkpoints/' + METHOD + "/encoder_lv1.pkl"))
torch.save(encoder_lv2.state_dict(),
str('./checkpoints/' + METHOD + "/encoder_lv2.pkl"))
torch.save(encoder_lv3.state_dict(),
str('./checkpoints/' + METHOD + "/encoder_lv3.pkl"))
torch.save(decoder_lv1.state_dict(),
str('./checkpoints/' + METHOD + "/decoder_lv1.pkl"))
torch.save(decoder_lv2.state_dict(),
str('./checkpoints/' + METHOD + "/decoder_lv2.pkl"))
torch.save(decoder_lv3.state_dict(),
str('./checkpoints/' + METHOD + "/decoder_lv3.pkl"))
